Field of the Invention
Embodiments of the subject matter disclosed herein generally relate to methods and systems and, more particularly, to mechanisms and techniques for diagnosing a machine in general, and a compressor in particular.
Description of the Prior Art
Today there are a large number of machines (industrial machines as, for example, compressors) installed at various facilities and used to process oil and gas. Such machines may experience symptoms that are indicative of a fault or a failure mode. Due to the technical complexities of these machines, the user of the machine might not have the capability to address these symptoms. Thus, the manufacturer of the machines, which has the technical capability to determine the matters affecting the machine, enters into maintenance and diagnostic agreements with the user for ensuring that the machines are monitored and maintained in an adequate state of operation. For this reason, the manufacturer of the machines may have plural sensors installed at the location of the user for monitoring the “health” of the machines. The same manufacturer may have plural contracts with multiple clients.
Prognostics and Health Management (PHM) is an emerging technology to support the efficient execution of contractual service agreements (CSA) for assets such as locomotives, medical scanners, aircraft engines, turbines, and compressors. One goal of PHM is to maintain these assets' operational performance over time, improving their utilization while minimizing their maintenance cost. PHM can be used as a product differentiator, to reduce the cost of the original equipment manufacturer service during the assets' warranty period, or to more efficiently provide service under a CSA.
FIG. 1 shows a traditional PHM system 10. According to this figure, after performing the traditional preparation tasks, such as sensor validation in a sensor validation unit 12 and input data pre-processing in a processing unit 14, the PHM system 10 performs anomaly detection and identification in unit 16, diagnostic analysis in unit 18, prognostic analysis in unit 20, fault accommodation in unit 22, and logistics decisions in unit 24. These actions are known by those skilled in the art and for this reason their detailed description is omitted herein.
The anomaly detection unit leverages unsupervised learning techniques, such as clustering. Its goal is to extract the underlying structural information from the data, define normal structures and identify departures from such normal structures. The diagnostics unit leverages supervised learning techniques, such as classification. Its goal is to extract potential signatures from the data, which could be used to recognize different faults/failure modes s. The prognostics unit produces estimates of Remaining Useful Life (RUL). Its goal is to maintain and forecast the asset health index. Originally, this index reflects the expected deterioration under normal operating conditions. Later the index is modified by the occurrence of an anomaly/failure, reflecting faster RUL reductions.
The above discussed functions are interpretations of the machine's state. These interpretations lead to an on-platform control action and an off-platform logistics, repair and planning action. The on-platform control actions are usually focused on maintaining performance or safety margins, and are performed in real-time. The off-platform maintenance/repair actions cover more complex offline decisions. They require a decision support system (DSS) performing multi-objective optimizations, exploring frontiers of corrective actions, and combining them with preference aggregations to generate the best decision tradeoffs.
However, the traditional algorithms for calculating the relevance of a determined diagnostic relative to the existent symptoms of the compressor are not always accurate and sometimes they are ambiguous. Accordingly, it would be desirable to provide systems and methods that avoid these problems and drawbacks.